AFS-YOLO: adaptive feature fusion and resampling for foggy object detection

Although object detection has been widely adopted in critical applications such as autonomous driving and security monitoring, its performance often deteriorates significantly under adverse weather conditions, including fog, rain, and snow. To address the challenges of object detection in foggy environments, this paper proposes the AFS-YOLO detection model. The proposed model improves detection accuracy and robustness without relying on additional dehazing preprocessing steps or external supervisory signals. Specifically, it incorporates an Adaptive Image Processing Module (AIPM) mechanism to better preserve image details, utilizes a Fog-Aware Adaptive Modulation (FAAM) module to dynamically optimize feature fusion, and introduces a Dynamic Shape-Context Intersection over Union (DSCIoU) loss function to enhance localization precision. Extensive experiments were conducted on two benchmark datasets—RTTS and Foggy-Cityscapes. The results demonstrate that AFS-YOLO achieves an overall mAP50 of 80.8% on RTTS and 56.4% on Foggy-Cityscapes, it demonstrates superior performance compared to the majority of existing methods for object detection in foggy conditions. Ablation studies further validate the effectiveness of each designed component in improving model performance. These findings comprehensively confirm the accuracy and robustness of AFS-YOLO in complex foggy scenarios.